A SINS/GNSS location method based on factor diagram is proposed to meet the requirement of accurate location of substation construction personnel. In this paper, the inertial autonomous positioning, carrier motion inf...A SINS/GNSS location method based on factor diagram is proposed to meet the requirement of accurate location of substation construction personnel. In this paper, the inertial autonomous positioning, carrier motion information acquisition and satellite positioning technologies are integrated. The factor graph method is adopted to abstract the measurement information received by inertial navigation and satellite into factor nodes, and the state information into variable nodes, so as to construct the SINS/GNSS construction personnel positioning fusion factor graph model. The Gauss-Newton iterative method is used to implement the recursive updating of variable nodes, and the optimal estimate of the location information of the construction personnel is calculated, which realized the high precision location of the construction personnel. The factor graph method is verified by pedestrian navigation data. The results show that the factor graph method can continuously and stably output high-precision positioning results, and realize non-equidistant fusion of SINS and GNSS. The positioning accuracy is better than Kalman filter algorithm, and the horizontal positioning accuracy is less than 1 m. Therefore, the factor graph method proposed can provide accurate location information for substation construction personnel.展开更多
Achieving accurate navigation information by integrating multiple sensors is key to the safe operation of land vehicles in global navigation satellite system(GNSS)-denied environment.However,current multi-sensor fusio...Achieving accurate navigation information by integrating multiple sensors is key to the safe operation of land vehicles in global navigation satellite system(GNSS)-denied environment.However,current multi-sensor fusion methods are based on stovepipe architecture,which is optimized with custom fusion strategy for specific sensors.Seeking to develop adaptable navigation that allows rapid integration of any combination of sensors to obtain robust and high-precision navigation solutions in GNSS-denied environment,we propose a generic plug-and-play fusion strategy to estimate land vehicle states.The proposed strategy can handle different sensors in a plug-and-play manner as sensors are abstracted and represented by generic models,which allows rapid reconfiguration whenever a sensor signal is additional or lost during operation.Relative estimations are fused with absolute sensors based on improved factor graph,which includes sensors’error parameters in the non-linear optimization process to conduct sensor online calibration.We evaluate the performance of our approach using a land vehicle equipped with a global positioning system(GPS)receiver as well as inertial measurement unit(IMU),camera,wireless sensor and odometer.GPS is not integrated into the system but treated as ground truth.Results are compared with the most common filtering-based fusion algorithm.It shows that our strategy can process low-quality input sources in a plug-and-play and robust manner and its performance outperforms filtering-based method in GNSS-denied environment.展开更多
地磁同步定位与构图(simultaneously localization and mapping,SLAM)无须先验地磁指纹库,即可实现基于智能手机的未知室内环境定位。然而,智能手机地磁SLAM仍受限于惯性定位精度差、因子图优化算法动态适应能力不足及大型场景SLAM应用...地磁同步定位与构图(simultaneously localization and mapping,SLAM)无须先验地磁指纹库,即可实现基于智能手机的未知室内环境定位。然而,智能手机地磁SLAM仍受限于惯性定位精度差、因子图优化算法动态适应能力不足及大型场景SLAM应用系统性能恶化等技术瓶颈。为解决此问题,本文通过设计方差时序递增机制和多源关键数据帧,提出一种面向大型室内场景的地磁SLAM增强优化算法。首先,为了提高惯性定位精度,本文挖掘行人运动过程中呈现出的特征规律构建观测方程,并融合地磁环境信息实现手机端地磁SLAM。然后,针对因子图优化算法动态适应能力不足,采用前端卡尔曼滤波与后端因子图优化相结合的定位框架提升时效性,同时设计方差时序递增机制,动态融合不同定位方法。最后,为了缓解大型场景地磁SLAM性能恶化,在时序维度上扩展关键帧概念和特征表达能力,有效缓解大型场景地磁误匹配问题;结合多源数据设计稳健回环探测与匹配算法,构建关键帧评分机制降低空间密度,从而提高算法效率。试验结果表明,本文实现了大型室内场景闭环情形下的地磁SLAM,相比惯性定位和经典地磁SLAM,本文提出的地磁SLAM增强优化方法的位置均方根误差降低了18%~67%;并且在仅利用标准方法22.6%的关键帧数量的前提下,本文方法仍能保持更高精度、更平滑的定位结果;通过试验探究了参数设置对定位精度和运行时间的影响,明确了地磁图构建首要因素基函数体素网格边长。展开更多
文摘A SINS/GNSS location method based on factor diagram is proposed to meet the requirement of accurate location of substation construction personnel. In this paper, the inertial autonomous positioning, carrier motion information acquisition and satellite positioning technologies are integrated. The factor graph method is adopted to abstract the measurement information received by inertial navigation and satellite into factor nodes, and the state information into variable nodes, so as to construct the SINS/GNSS construction personnel positioning fusion factor graph model. The Gauss-Newton iterative method is used to implement the recursive updating of variable nodes, and the optimal estimate of the location information of the construction personnel is calculated, which realized the high precision location of the construction personnel. The factor graph method is verified by pedestrian navigation data. The results show that the factor graph method can continuously and stably output high-precision positioning results, and realize non-equidistant fusion of SINS and GNSS. The positioning accuracy is better than Kalman filter algorithm, and the horizontal positioning accuracy is less than 1 m. Therefore, the factor graph method proposed can provide accurate location information for substation construction personnel.
基金partially supported by the National Natural Science Foundation of China(No. 61703207)the Jiangsu Provincial Natural Science Founda- tion of China(No. BK20170801)+2 种基金the Aeronautical Science Foundation of China(No. 2017ZC52017)the Jiangsu Provincial SixTalent Peaks(No. 2015-XXRJ-005)the Jiangsu Province Qing Lan Project
文摘Achieving accurate navigation information by integrating multiple sensors is key to the safe operation of land vehicles in global navigation satellite system(GNSS)-denied environment.However,current multi-sensor fusion methods are based on stovepipe architecture,which is optimized with custom fusion strategy for specific sensors.Seeking to develop adaptable navigation that allows rapid integration of any combination of sensors to obtain robust and high-precision navigation solutions in GNSS-denied environment,we propose a generic plug-and-play fusion strategy to estimate land vehicle states.The proposed strategy can handle different sensors in a plug-and-play manner as sensors are abstracted and represented by generic models,which allows rapid reconfiguration whenever a sensor signal is additional or lost during operation.Relative estimations are fused with absolute sensors based on improved factor graph,which includes sensors’error parameters in the non-linear optimization process to conduct sensor online calibration.We evaluate the performance of our approach using a land vehicle equipped with a global positioning system(GPS)receiver as well as inertial measurement unit(IMU),camera,wireless sensor and odometer.GPS is not integrated into the system but treated as ground truth.Results are compared with the most common filtering-based fusion algorithm.It shows that our strategy can process low-quality input sources in a plug-and-play and robust manner and its performance outperforms filtering-based method in GNSS-denied environment.
文摘地磁同步定位与构图(simultaneously localization and mapping,SLAM)无须先验地磁指纹库,即可实现基于智能手机的未知室内环境定位。然而,智能手机地磁SLAM仍受限于惯性定位精度差、因子图优化算法动态适应能力不足及大型场景SLAM应用系统性能恶化等技术瓶颈。为解决此问题,本文通过设计方差时序递增机制和多源关键数据帧,提出一种面向大型室内场景的地磁SLAM增强优化算法。首先,为了提高惯性定位精度,本文挖掘行人运动过程中呈现出的特征规律构建观测方程,并融合地磁环境信息实现手机端地磁SLAM。然后,针对因子图优化算法动态适应能力不足,采用前端卡尔曼滤波与后端因子图优化相结合的定位框架提升时效性,同时设计方差时序递增机制,动态融合不同定位方法。最后,为了缓解大型场景地磁SLAM性能恶化,在时序维度上扩展关键帧概念和特征表达能力,有效缓解大型场景地磁误匹配问题;结合多源数据设计稳健回环探测与匹配算法,构建关键帧评分机制降低空间密度,从而提高算法效率。试验结果表明,本文实现了大型室内场景闭环情形下的地磁SLAM,相比惯性定位和经典地磁SLAM,本文提出的地磁SLAM增强优化方法的位置均方根误差降低了18%~67%;并且在仅利用标准方法22.6%的关键帧数量的前提下,本文方法仍能保持更高精度、更平滑的定位结果;通过试验探究了参数设置对定位精度和运行时间的影响,明确了地磁图构建首要因素基函数体素网格边长。
